lightgbm.train()函数执行直接训练。 lightgbm.train(params,train_set,num_boost_round=100,valid_sets=None,valid_names=None,fobj=None,feval=None,init_model=None,feature_name='auto',categorical_feature='auto',early_stopping_rounds=None,evals_result=None,verbose_eval=True,learning_rates=None,keep_tra...
lgb_train = lgb.Dataset(X_train, y_train)lgb_test = lgb.Dataset(X_test, y_test, reference=lgb_train)# 设定参数params = {'num_leaves': 5,'metric': ('l1', 'l2'),'verbose': 0}evals_result = {} # to record eval results for plottingprint('开始训练...')# 训练gbm = lgb....
lightgbm.train(params,train_set,num_boost_round=100,valid_sets=None,valid_names=None,fobj=None,feval=None,init_model=None,feature_name='auto',categorical_feature='auto',early_stopping_rounds=None,evals_result=None,verbose_eval=True,learning_rates=None,keep_training_booster=False,callbacks=None,sh...
train's binary_logloss: 0.21758 train's auc: 0.995984 valid's binary_logloss: 0.244315 valid's auc: 0.993806 test's binary_logloss: 0.223847 test's auc: 0.994231 准确率: 0.9651 ROC AUC分数: 0.9942 Log Loss: 0.2238 # 绘制训练、验证和测试集损失曲线 train_loss = model.evals_result_['trai...
train_data, valid_sets=[train_data, test_data], valid_names=['train', 'test'], num_boost_round=1000, early_stopping_rounds=50, verbose_eval=50, evals_result=evals_result ) 4 模型评估与可视化 #预测并计算RMSE y_pred = model.predict(X_test) ...
data或者train或者train_data:一个字符串,给出了训练数据所在的文件的文件名。默认为空字符串。LightGBM将使用它来训练模型。 valid或者test或者valid_data或者test_data:一个字符串,表示验证集所在的文件的文件名。默认为空字符串。LightGBM将输出该数据集的度量。如果有多个验证集...
values # 构建lgb中的Dataset数据格式 lgb_train = lgb.Dataset(X_train, y_train) lgb_test = lgb.Dataset(X_test, y_test, reference=lgb_train) # 设定参数 params = { 'num_leaves': 5, 'metric': ('l1', 'l2'), 'verbose': 0 } evals_result = {} # to record eval results for ...
lightgbm.train() 函数执行直接训练。 lightgbm.train(params, train_set, num_boost_round=100, valid_sets=None, valid_names=None, fobj=None, feval=None, init_model=None, feature_name='auto', categorical_feature='auto', early_stopping_rounds=None, evals_result=None, verbose_eval=True, learnin...
evals_result= {}#记录训练结果所用gbm_model =lgb.train(parameters, lgb_train, valid_sets=[lgb_train,lgb_eval], num_boost_round=50,#提升迭代的次数early_stopping_rounds=5, evals_result=evals_result, verbose_eval=10) prediction= gbm_model.predict(x_test,num_iteration=gbm_model.best_iteration...
Bigboboboy 初级粉丝 1 然后,图三是train源码部分,好像没有evals_result参数。CPU版本绘制loss曲线的一般思路,也是导出evals_result来绘制。那他没有怎么绘制呢 Bigboboboy 初级粉丝 1 顶顶顶,今天装完lightgbm,捣鼓了一天了。求求用过的大佬解答下 登录...